Change analysis on evolving PLC software in automated production systems

Abstract Control software for automated Production Systems (aPSs) becomes increasingly complex. Respective systems undergo constant evolution. Yet, proper documentation may not always be present, entailing maintenance issues in the long run. While manual examination of software for aPSs is an error-prone task, static analysis can improve system quality. However, it has not been applied to describe software evolution by means of changed systems artifacts. The authors address this issue and perform change analyses on IEC61131-3 projects, identifying introduced and removed systems artifacts as well as existing ones affected. By that, the authors aim to support sustainable evolution. Two feasibility studies, implemented independently, but for the same evolution scenarios for an automation plant, are used for evaluation. The technique is shown to be efficient and highly precise.

Download Full-text

MICOSE4aPS: Industrially Applicable Maturity Metric to Improve Systematic Reuse of Control Software

ACM Transactions on Software Engineering and Methodology ◽

10.1145/3467896 ◽

2022 ◽

Vol 31 (1) ◽

pp. 1-24

Author(s):

Birgit Vogel-Heuser ◽

Eva-Maria Neumann ◽

Juliane Fischer

Keyword(s):

Food Packaging ◽

Manufacturing Industry ◽

Production Systems ◽

System Quality ◽

Control Software ◽

Mechatronic Systems ◽

Automated Production ◽

Quality Of Control ◽

Software Modules ◽

Systematic Reuse

automated Production Systems (aPS) are highly complex, mechatronic systems that usually have to operate reliably for many decades. Standardization and reuse of control software modules is a core prerequisite to achieve the required system quality in increasingly shorter development cycles. However, industrial case studies in aPS show that many aPS companies still struggle with strategically reusing software. This paper proposes a metric-based approach to objectively measure the m aturity of i ndustrial IEC 61131-based co ntrol s oftwar e in aPS (MICOSE4aPS) to identify potential weaknesses and quality issues hampering systematic reuse. Module developers in the machine and plant manufacturing industry can directly benefit as the metric calculation is integrated into the software engineering workflow. An in-depth industrial evaluation in a top-ranked machine manufacturing company in food packaging and an expert evaluation with different companies confirmed the benefit of efficiently managing the quality of control software.

Download Full-text

Success Factors for the Design of Field-level Control Code in Machine and Plant Manufacturing - an Industrial Survey -

10.21203/rs.3.rs-168613/v1 ◽

2021 ◽

Author(s):

Birgit Vogel-Heuser ◽

Juliane Fischer ◽

Eva-Maria Neumann ◽

Matthias Kreiner

Keyword(s):

Software Engineering ◽

Success Factors ◽

Production Systems ◽

Development Effort ◽

Manufacturing Companies ◽

Level Control ◽

Control Software ◽

Automated Production ◽

Field Level ◽

Control Code

Abstract The amount of software in automated production systems, including its development effort, is continuously increasing to currently up to 35-50% of the development personnel. Consequently, success factors for achieving modularity and complexity management of control software are of high economic interest. Scientific solutions are manifold but often not implemented in industry. This paper introduces the study QoaPS SWE (Quality of automated Production Systems’ Software Engineering) providing insights into 61 machine and plant manufacturing companies to give quantitative and qualitative results to five essential research questions on success factors in the design of field-level control code. Compared to preceding surveys, QoaPS SWE achieves statistically significant results for software maturity (MMOD+), complexity, and model-based software engineering and provides detailed insights into causes and consequences for single criteria, thus clearly identifying obstacles to be addressed in future research and with industrial countermeasures. Especially staff qualification and organizational issues are identified as obstacles to applying the object-oriented programming paradigm for control software in machine and plant manufacturing. Validity is ensured by analyzing the statistical significance of the results in addition to comparisons with earlier surveys and interviews as well as the comparison with already existing and accepted maturity levels. The provided qualitative and quantitative results will allow the benchmarking of companies’ maturity and the derivation of concrete recommendations for companies depending on their MMOD+ value and the evaluated characteristics.

Download Full-text

Effect of Failure Rate on the Availability Analysis of Nestle CERELAC Plant Using Markov Chain Method

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset2184116 ◽

2018 ◽

pp. 121-127

Author(s):

Gurbinder Singh ◽

Rakesh Kumar

Keyword(s):

Markov Chain ◽

Manufacturing Systems ◽

Production Systems ◽

Research Work ◽

Automated Production ◽

Availability Analysis ◽

Long Run ◽

Machine Reliability ◽

Markov Chain Method

In the performance analysis of production systems by using the traditional methods of engineering the knowledge of machine reliability factors is assumed to be precisely known. The current study entitled performance evaluation of food industry in India. To analyze and determine the availability of plant a case study has been undertaken from Moga Nestle food private limited industry in India. Various studies evaluating the performance of automated production systems with the help of modeling and simulation and analytical methods have always given priority to steady state performance as compared to transient performance. Production systems in which such kind of situations arises include systems with dysfunctional states and deadlocks, not stable queuing systems. This research work presents an approach for analyzing the performance of unreliable manufacturing systems that take care of uncertain machine factor estimates. The method that is being proposed is on the basis of Markov chain and probability density function discretization techniques for studying manufacture lines consist unreliable machines. To determine the performance of plant, important information has been collected from different systems and subsystems to find out long run availability of whole system.

Download Full-text